A cellular DNA-binding protein that activates eukaryotic transcription and DNA replication

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms

DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.

LGC-DBP: the method of DNA-binding protein identification based on PSSM and deep learning

The recognition of DNA Binding Proteins (DBPs) plays a crucial role in understanding biological functions such as replication, transcription, and repair. Although current sequence-based methods have shown some effectiveness, they often fail to fully utilize the potential of deep learning in capturing complex patterns. This study introduces a novel model, LGC-DBP, which integrates Long Short-Term Memory (LSTM), Gated Inception Convolution, and Improved Channel Attention mechanisms to enhance the prediction of DBPs. Initially, the model transforms protein sequences into Position Specific Scoring Matrices (PSSM), then processed through our deep learning framework. Within this framework, Gated Inception Convolution merges the concepts of gating units with the advantages of Graph Convolutional Network (GCN) and Dilated Convolution, significantly surpassing traditional convolution methods. The Improved Channel Attention mechanism substantially enhances the model's responsiveness and accuracy by shifting from a single input to three inputs and integrating three sigmoid functions along with an additional layer output. These innovative combinations have significantly improved model performance, enabling LGC-DBP to recognize and interpret the complex relationships within DBP features more accurately. The evaluation results show that LGC-DBP achieves an accuracy of 88.26% and a Matthews correlation coefficient of 0.701, both surpassing existing methods. These achievements demonstrate the model's strong capability in integrating and analyzing multi-dimensional data and mark a significant advancement over traditional methods by capturing deeper, nonlinear interactions within the data.

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction

DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-binding proteins play a crucial role in cellular and molecular biology, as they are essential for maintaining normal cellular physiological functions and adapting to environmental changes. The prediction of DNA-binding proteins has been a hot topic in the field of bioinformatics. The key to accurately classifying DNA-binding proteins is to find suitable feature sources and explore the information they contain. Although there are already many models for predicting DNA-binding proteins, there is still room for improvement in mining feature source information and calculation methods. In this study, we created a model called DBPboost to better identify DNA-binding proteins. The innovation of this study lies in the use of eight feature extraction methods, the improvement of the feature selection step, which involves selecting some features first and then performing feature selection again after feature fusion, and the optimization of the differential evolution algorithm in feature fusion, which improves the performance of feature fusion. The experimental results show that the prediction accuracy of the model on the UniSwiss dataset is 89.32%, and the sensitivity is 89.01%, which is better than most existing models.

Prediction of hot spots in protein-DNA binding interfaces based on discrete wavelet transform and wavelet packet transform

BACKGROUND

Identification of hot spots in protein-DNA binding interfaces is extremely important for understanding the underlying mechanisms of protein-DNA interactions and drug design. Since experimental methods for identifying hot spots are time-consuming and expensive, and most of the existing computational methods are based on traditional protein-DNA features to predict hot spots, unable to make full use of the effective information in the features.

RESULTS

In this work, a method named WTL-PDH is proposed for hot spots prediction. To deal with the unbalanced dataset, we used the Synthetic Minority Over-sampling Technique to generate minority class samples to achieve the balance of dataset. First, we extracted the solvent accessible surface area features and structural features, and then processed the traditional features using discrete wavelet transform and wavelet packet transform to extract the wavelet energy information and wavelet entropy information, and obtained a total of 175 dimensional features. In order to obtain the best feature subset, we systematically evaluate these features in various feature selection strategies. Finally, light gradient boosting machine (LightGBM) was used to establish the model.

CONCLUSIONS

Our method achieved good results on independent test set with AUC, MCC and F1 scores of 0.838, 0.533 and 0.750, respectively. WTL-PDH can achieve generally better performance in predicting hot spots when compared with state-of-the-art methods. The dataset and source code are available at https://github.com/chase2555/WTL-PDH .

MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins

DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is a crucial issue. And it is greatly helpful to understand the mechanism of protein-DNA interactions. In traditional experimental methods, it is significant time-consuming and labor-consuming to identify DBPs. In recent years, many researchers have proposed lots of different DBP identification methods based on machine learning algorithm to overcome shortcomings mentioned above. However, most existing methods cannot get satisfactory results. In this paper, we focus on developing a new predictor of DBPs, called Multi-View Hypergraph Restricted Kernel Machines (MV-H-RKM). In this method, we extract five features from the three views of the proteins. To fuse these features, we couple them by means of the shared hidden vector. Besides, we employ the hypergraph regularization to enforce the structure consistency between original features and the hidden vector. Experimental results show that the accuracy of MV-H-RKM is 84.09% and 85.48% on PDB1075 and PDB186 data set respectively, and demonstrate that our proposed method performs better than other state-of-the-art approaches. The code is publicly available at https://github.com/ShixuanGG/MV-H-RKM.

Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm

Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational methods for predicting the DBPs based on protein sequences have obtained great success, there is still room for improvement since the sequence-order information is not fully mined in these methods. In this study, a new three-part sequence-order feature extraction (called TPSO) strategy is developed to extract more discriminative information from protein sequences for predicting the DBPs. For each query protein, TPSO first divides its primary sequence features into N- and C-terminal fragments and then extracts the numerical pseudo features of three parts including the full sequence and these two fragments, respectively. Based on TPSO, a novel deep learning-based method, called TPSO-DBP, is proposed, which employs the sequence-based single-view features, the bidirectional long short-term memory (BiLSTM) and fully connected (FC) neural networks to learn the DBP prediction model. Empirical outcomes reveal that TPSO-DBP can achieve an accuracy of 87.01%, covering 85.30% of all DBPs, while achieving a Matthew's correlation coefficient value (0.741) that is significantly higher than most existing state-of-the-art DBP prediction methods. Detailed data analyses have indicated that the advantages of TPSO-DBP lie in the utilization of TPSO, which helps extract more concealed prominent patterns, and the deep neural network framework composed of BiLSTM and FC that learns the nonlinear relationships between input features and DBPs. The standalone package and web server of TPSO-DBP are freely available at https://jun-csbio.github.io/TPSO-DBP/.

Molecular Characteristics and Promoter Analysis of Porcine COL1A1

COL1A1 encodes the type I collagen α1 chain, which shows the highest abundance among members of the collagen family and is widely expressed in different mammalian cells and tissues. However, its molecular characteristics are not completely elucidated. In this study, the molecular profiles of COL1A1 and characteristics of the COL1A1 protein were investigated using a promoter activity assay and multiple bioinformatics tools. The results showed that the 5' flanking region of porcine COL1A1 contained two CpG islands, five core promoter sequences, and twenty-six transcription factor-binding sites. In the luciferase assay, the upstream 294 bp region of the initiation codon of COL1A1 showed the highest activity, confirming that this section is the core region of the porcine COL1A1 promoter. Bioinformatic analysis revealed that COL1A1 is a negatively charged, hydrophilic secreted protein. It does not contain a transmembrane domain and is highly conserved in humans, mice, sheep, and pigs. Protein interaction analysis demonstrated that the interaction coefficient of COL1A1 with COL1A2, COL3A1, ITGB1, and ITGA2 was greater than 0.9, suggesting that this protein plays a crucial role in collagen structure formation and cell adhesion. These results provide a theoretical basis for further investigation of the functions of porcine COL1A1.

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

In the domain of genome annotation, the identification of DNA-binding protein is one of the crucial challenges. DNA is considered a blueprint for the cell. It contained all necessary information for building and maintaining the trait of an organism. It is DNA, which makes a living thing, a living thing. Protein interaction with DNA performs an essential role in regulating DNA functions such as DNA repair, transcription, and regulation. Identification of these proteins is a crucial task for understanding the regulation of genes. Several methods have been developed to identify the binding sites of DNA and protein depending upon the structures and sequences, but they were costly and time-consuming. Therefore, we propose a methodology named “DNAPred_Prot”, which uses various position and frequency-dependent features from protein sequences for efficient and effective prediction of DNA-binding proteins. Using testing techniques like 10-fold cross-validation and jackknife testing an accuracy of 94.95% and 95.11% was yielded, respectively. The results of SVM and ANN were also compared with those of a random forest classifier. The robustness of the proposed model was evaluated by using the independent dataset PDB186, and an accuracy of 91.47% was achieved by it. From these results, it can be predicted that the suggested methodology performs better than other extant methods for the identification of DNA-binding proteins.

Improved datasets and evaluation methods for the automatic prediction of DNA-binding proteins

MOTIVATION

Accurate automatic annotation of protein function relies on both innovative models and robust datasets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the datasets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the datasets used in previous DNA-binding protein literature and provide several new datasets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved datasets to two previously published models. In addition, we provide extensive tests showing how the best models predict across taxa.

RESULTS

Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxa, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms.

AVAILABILITY AND IMPLEMENTATION

The data and results for this article can be found at https://doi.org/10.5281/zenodo.5153906. The code for this article can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.

An improved DNA-binding hot spot residues prediction method by exploring interfacial neighbor properties

Background

DNA-binding hot spots are dominant and fundamental residues that contribute most of the binding free energy yet accounting for a small portion of protein–DNA interfaces. As experimental methods for identifying hot spots are time-consuming and costly, high-efficiency computational approaches are emerging as alternative pathways to experimental methods.

Results

Herein, we present a new computational method, termed inpPDH, for hot spot prediction. To improve the prediction performance, we extract hybrid features which incorporate traditional features and new interfacial neighbor properties. To remove redundant and irrelevant features, feature selection is employed using a two-step feature selection strategy. Finally, a subset of 7 optimal features are chosen to construct the predictor using support vector machine. The results on the benchmark dataset show that this proposed method yields significantly better prediction accuracy than those previously published methods in the literature. Moreover, a user-friendly web server for inpPDH is well established and is freely available at http://bioinfo.ahu.edu.cn/inpPDH.

Conclusions

We have developed an accurate improved prediction model, inpPDH, for hot spot residues in protein–DNA binding interfaces by given the structure of a protein–DNA complex. Moreover, we identify a comprehensive and useful feature subset including the proposed interfacial neighbor features that has an important strength for identifying hot spot residues. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of interfacial neighbor features and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues in protein–DNA complexes.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12859-020-03871-1.

TargetDBP+: Enhancing the Performance of Identifying DNA-Binding Proteins via Weighted Convolutional Features

Protein-DNA interactions exist ubiquitously and play important roles in the life cycles of living cells. The accurate identification of DNA-binding proteins (DBPs) is one of the key steps to understand the mechanisms of protein-DNA interactions. Although many DBP identification methods have been proposed, the current performance is still unsatisfactory. In this study, a new method, called TargetDBP+, is developed to further enhance the performance of identifying DBPs. In TargetDBP+, five convolutional features are first extracted from five feature sources, i.e., amino acid one-hot matrix (AAOHM), position-specific scoring matrix (PSSM), predicted secondary structure probability matrix (PSSPM), predicted solvent accessibility probability matrix (PSAPM), and predicted probabilities of DNA-binding sites (PPDBSs); second, the five features are weightedly and serially combined using the weights of all of the elements learned by the differential evolution algorithm; and finally, the DBP identification model of TargetDBP+ is trained using the support vector machine (SVM) algorithm. To evaluate the developed TargetDBP+ and compare it with other existing methods, a new gold-standard benchmark data set, called UniSwiss, is constructed, which consists of 4881 DBPs and 4881 non-DBPs extracted from the UniprotKB/Swiss-Prot database. Experimental results demonstrate that TargetDBP+ can obtain an accuracy of 85.83% and precision of 88.45% covering 82.41% of all DBP data on the independent validation subset of UniSwiss, with the MCC value (0.718) being significantly higher than those of other state-of-the-art control methods. The web server of TargetDBP+ is accessible at http://csbio.njust.edu.cn/bioinf/targetdbpplus/; the UniSwiss data set and stand-alone program of TargetDBP+ are accessible at https://github.com/jun-csbio/TargetDBPplus.

TargetDBP: Accurate DNA-Binding Protein Prediction Via Sequence-Based Multi-View Feature Learning

Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences. In TargetDBP, four single-view features, i.e., AAC (Amino Acid Composition), PsePSSM (Pseudo Position-Specific Scoring Matrix), PsePRSA (Pseudo Predicted Relative Solvent Accessibility), and PsePPDBS (Pseudo Predicted Probabilities of DNA-Binding Sites), are first extracted to represent different base features, respectively. Second, differential evolution algorithm is employed to learn the weights of four base features. Using the learned weights, we weightedly combine these base features to form the original super feature. An excellent subset of the super feature is then selected by using a suitable feature selection algorithm SVM-REF+CBR (Support Vector Machine Recursive Feature Elimination with Correlation Bias Reduction). Finally, the prediction model is learned via using support vector machine on the selected feature subset. We also construct a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed TargetDBP with other existing predictors. On this new dataset, TargetDBP can achieve higher performance than other state-of-the-art predictors. The TargetDBP web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/targetdbp/ for academic use.

HMMPred: Accurate Prediction of DNA-Binding Proteins Based on HMM Profiles and XGBoost Feature Selection

Prediction of DNA-binding proteins (DBPs) has become a popular research topic in protein science due to its crucial role in all aspects of biological activities. Even though considerable efforts have been devoted to developing powerful computational methods to solve this problem, it is still a challenging task in the field of bioinformatics. A hidden Markov model (HMM) profile has been proved to provide important clues for improving the prediction performance of DBPs. In this paper, we propose a method, called HMMPred, which extracts the features of amino acid composition and auto- and cross-covariance transformation from the HMM profiles, to help train a machine learning model for identification of DBPs. Then, a feature selection technique is performed based on the extreme gradient boosting (XGBoost) algorithm. Finally, the selected optimal features are fed into a support vector machine (SVM) classifier to predict DBPs. The experimental results tested on two benchmark datasets show that the proposed method is superior to most of the existing methods and could serve as an alternative tool to identify DBPs.

A Review of DNA-binding Proteins Prediction Methods

Background:

DNA-binding proteins, binding to DNA, widely exist in living cells, participating in many cell activities. They can participate some DNA-related cell activities, for instance DNA replication, transcription, recombination, and DNA repair.

Objective:

Given the importance of DNA-binding proteins, studies for predicting the DNA-binding proteins have been a popular issue over the past decades. In this article, we review current machine-learning methods which research on the prediction of DNA-binding proteins through feature representation methods, classifiers, measurements, dataset and existing web server.

Method:

The prediction methods of DNA-binding protein can be divided into two types, based on amino acid composition and based on protein structure. In this article, we accord to the two types methods to introduce the application of machine learning in DNA-binding proteins prediction.

Results:

Machine learning plays an important role in the classification of DNA-binding proteins, and the result is better. The best ACC is above 80%.

Conclusion:

Machine learning can be widely used in many aspects of biological information, especially in protein classification. Some issues should be considered in future work. First, the relationship between the number of features and performance must be explored. Second, many features are used to predict DNA-binding proteins and propose solutions for high-dimensional spaces.

Investigation of Cytotoxic T-lymphocyte antigen-4 polymorphisms in non-small cell lung cancer: a case-control study

The objective of this case-control study was to extensively explore the relationship of Cytotoxic T-lymphocyte antigen-4 (CTLA-4) tagging polymorphisms with susceptibility to non-small-cell lung cancer (NSCLC). We recruited 521 sporadic NSCLC cases and 1,030 non-cancer controls. The genotypes of CTLA-4 rs16840252 C>T, rs231775 G>A, rs3087243 G>A and rs733618 T>C polymorphisms were evaluated using a custom-by-design 48-Plex SNPscan Kit. Our findings revealed there was no statistically significant difference in CTLA-4 genotypes distribution among NSCLC patients and non-cancer controls. Similar findings were observed in the logistic regression analyses. However, the stratified analyses suggested CTLA-4 rs733618 vatiants were correlated with the development of NSCLC in ≥ 60 years subgroup (TC vs. TT: adjusted OR = 1.45, 95% CI = 1.04–2.02, P = 0.030) and even drinking subgroup (TC vs. TT: adjusted OR = 2.27, 95% CI = 1.11–4.60, P = 0.024 and TC/CC vs. TT: adjusted OR = 2.26, 95% CI = 1.15–4.43, P = 0.018). In conclusion, the present case-control study highlights that the CTLA-4 rs733618 T>C polymorphism was associated with the development of NSCLC in ≥ 60 years and even drinking subgroups. A fine-mapping study with functional assessment is necessary to confirm or refute our findings.

The role of nuclear factor I-C in tooth and bone development

Nuclear factor I-C (NFI-C) plays a pivotal role in various cellular processes such as odontoblast and osteoblast differentiation. Nfic-deficient mice showed abnormal tooth and bone formation. The transplantation of Nfic-expressing mouse bone marrow stromal cells rescued the impaired bone formation in Nfic^-/- mice. Studies suggest that NFI-C regulate osteogenesis and dentinogenesis in concert with several factors including transforming growth factor-β1, Krüppel-like factor 4, and β-catenin. This review will focus on the function of NFI-C during tooth and bone formation and on the relevant pathways that involve NFI-C.

Hepatitis B virus surface protein-induced hPIAS1 transcription requires TAL1, E47, MYOG, NFI, and MAPK signal pathways

The protein inhibitor of activated STAT1 (PIAS1) plays important roles in regulating virus-induced chronic hepatitis, but the interaction between hepatitis B virus (HBV) and hPIAS1 is not clear. Our aim was to verify if HBV encoding proteins enhance the transcription of hPIAS1 and which cis-elements and transcription factors were involved in the mechanism. In order to do, so a series of molecular biological methods, along with functional and histological studies, were performed. We found that the HBV surface protein (HBs) enhanced hPIAS1 transcription through the activities of TAL1, E47, myogenin (MYOG), and NFI, dependent on the activation of p38MAPK and ERK signaling pathways in vitro, which might contribute to the ineffectiveness of treatment in CHB patients. Furthermore, liver samples from patients with high HBsAg levels and HBV DNA displayed increased hPIAS1 expression and high levels of TAL1, E47, MYOG, and NFI, compared to those patients with low HBsAg levels and HBV DNA, and healthy controls. These findings suggest that the HBs protein-induced hPIAS1 transcription requires TAL1, E47, MYOG, NFI, and MAPK signal pathways. It provides new potential targets for antiviral therapeutic strategies for controlling HBV-associated diseases.

Galeterone and VNPT55 disrupt Mnk-eIF4E to inhibit prostate cancer cell migration and invasion

Metastatic castration-resistant prostate cancer (mCRPC) accounts for a high percentage of prostate cancer mortality. The proprietary compound galeterone (gal) was designed to inhibit proliferation of androgen/androgen receptor (AR)-dependent prostate cancer cell in vitro and in vivo and is currently in phase III clinical development. Additionally, clinical studies with gal revealed its superb efficacy in four different cohorts of patients with mCRPC, including those expressing splice variant AR-V7. Preclinical studies with gal show that it also exhibits strong antiproliferative activities against AR-negative prostate cancer cells and tumors through a mechanism involving phosphorylation of eIF2α, which forms an integral component of the eukaryotic mRNA translation complex. Thus, we hypothesized that gal and its new analog, VNPT55, could modulate oncogenic mRNA translation and prostate cancer cell migration and invasion. We report that gal and VNPT55 profoundly inhibit migration and invasion of prostate cancer cells, possibly by down-regulating protein expression of several EMT markers (Snail, Slug, N-cadherin, vimentin, and MMP-2/-9) via antagonizing the Mnk-eIF4E axis. In addition, gal/VNPT55 inhibited both NF-κB and Twist1 transcriptional activities, down-regulating Snail and BMI-1 mRNA expression, respectively. Furthermore, profound up-regulation of E-cadherin mRNA and protein expression may explain the observed significant inhibition of prostate cancer cell migration and invasion. Moreover, expression of self-renewal proteins, β-catenin, CD44, and Nanog, was markedly depleted. Analysis of gal/VNPT55-treated CWR22Rv1 xenograft tissue sections also revealed that observations in vitro were recapitulated in vivo. Our results suggest that gal/VNPT55 could become promising agents for the prevention and/or treatment of all stages of prostate cancer.

Human RNA Polymerase II Promoter Recruitment in Vitro Is Regulated by O-Linked N-Acetylglucosaminyltransferase (OGT)

Although the O-linked N-acetylglucosamine (O-GlcNAc) modification of the RNA polymerase II C-terminal domain was described 20 years ago, the function of this RNA polymerase II (pol II) species is not known. We show here that an O-GlcNAcylated pol II species (pol IIγ) exists on promoters in vitro Inhibition of O-GlcNAc-transferase activity and O-GlcNAcylation prevents pol II entry into the promoter, and O-GlcNAc removal from pol II is an ATP-dependent step during initiation. These data indicate that O-GlcNAc-transferase activity is essential for RNA pol II promoter recruitment and that pol II goes through a cycling of O-GlcNAcylation at the promoter. Mass spectrometry shows that serine residues 2 and 5 of the pol II C-terminal domain are O-GlcNAcylated, suggesting an overlap with the transcription factor IIH (TFIIH)-dependent serine 5 phosphorylation events during initiation and P-TEFb (positive transcriptional elongation factor b) events during elongation. These data provide unexpected and important insights into the role of a previously ill-defined species of RNA polymerase II in regulating transcription.

DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representation

DNA-binding proteins play an important role in most cellular processes. Therefore, it is necessary to develop an efficient predictor for identifying DNA-binding proteins only based on the sequence information of proteins. The bottleneck for constructing a useful predictor is to find suitable features capturing the characteristics of DNA binding proteins. We applied PseAAC to DNA binding protein identification, and PseAAC was further improved by incorporating the evolutionary information by using profile-based protein representation. Finally, Combined with Support Vector Machines (SVMs), a predictor called iDNAPro-PseAAC was proposed. Experimental results on an updated benchmark dataset showed that iDNAPro-PseAAC outperformed some state-of-the-art approaches, and it can achieve stable performance on an independent dataset. By using an ensemble learning approach to incorporate more negative samples (non-DNA binding proteins) in the training process, the performance of iDNAPro-PseAAC was further improved. The web server of iDNAPro-PseAAC is available at http://bioinformatics.hitsz.edu.cn/iDNAPro-PseAAC/.

Lack of association between cytotoxic T-lymphocyte antigen 4 (CTLA-4) -1722T/C (rs733618) polymorphism and cancer risk: from a case-control study to a meta-analysis

BACKGROUND

The association between cytotoxic T-lymphocyte antigen 4 (CTLA-4) gene -1722T/C polymorphism (rs733618) and cancer has been widely assessed, and a definitive conclusion remains elusive. We first performed a hospital based case-control study to measure this association of esophageal cancer with CTLA-4 -1722T/C polymorphism in Han Chinese population, and then carried out a meta-analysis to obtain a comprehensive evaluation for this issue.

METHODOLOGY/PRINCIPAL FINDINGS

This case-control study involved 629 esophageal squamous cell carcinoma (ESCC) cases and 686 age and gender well matched cancer-free controls. PCR-LDR (polymerase chain reaction-ligase detection reactions) method was used to identify genotypes. Meta-analysis was conducted by STATA (v12.0) software. This case-control study showed no significant difference in the genotype and allele distributions of CTLA-4 -1722T/C polymorphism between esophageal cancer cases and control subjects, in accord with the findings of the further meta-analysis in all genetic models. Evidence of large heterogeneity was observed among all eligible studies in the recessive model. Further subgroup analyses by ethnicity, cancer type and system, detected null associations in this meta-analysis.

CONCLUSION

This case-control study and the further meta-analysis, failed to identify the association between CTLA-4 -1722T/C polymorphism and cancer risk.

Association between cytotoxic T lymphocyte antigen-4 +49A/G, -1722T/C, and -1661A/G polymorphisms and cancer risk: a meta-analysis

Cytotoxic T lymphocyte antigen-4 (CTLA-4), a key gene that contributes to the susceptibility and clinical course of cancer, is an important down-regulator of T cell activation and proliferation. The +49A/G polymorphism is commonly studied because of its association with cancer risks. However, other polymorphisms, such as -1722T/C and -1661A/G, have not been studied in detail. We performed a meta-analysis using 43 eligible case-control studies with a total of 19,089 patients and 21,388 controls to examine the association between CTLA-4 +49A/G, -1722T/C, and -1661A/G polymorphisms and cancer risk. We searched the PubMed and EMBASE databases for all articles published up to July 17, 2013. Individuals with the +49 A allele (AA/AG vs. GG, odds ratio (OR) = 1.21, 95% confidence interval (95% CI) = 1.16-1.27) and -1661 G allele (AG/GG vs. AA, OR = 1.52, 95% CI = 1.34-1.73) had increased cancer risk. However, no significant association between cancer risk and the -1722T/C polymorphism was found (CC/CT vs. TT, OR = 1.04, 95% CI = 0.92-1.16). In subgroup analysis for the +49A/G polymorphism, increased cancer risk remained in the subgroups of Asians (OR = 1.25, 95 % CI = 1.18-1.31), patients with breast cancer (OR = 1.28, 95% CI = 1.15-1.42), and patients with lung cancer (OR = 1.20, 95 % CI = 1.07-1.35). For the -1661A/G polymorphism, increased cancer risk remained in the subgroups of Asians (OR = 1.52, 95% CI = 1.34-1.73), patients with breast cancer (OR = 1.48, 95% CI = 1.07-2.03), and patients with oral cancer (OR = 3.16, 95% CI = 1.84-5.45). However, no significant increase in cancer risk was found in the subgroups for the -1722T/C polymorphism. In conclusion, the results suggest that +49A/G and -1661A/G polymorphisms in CTLA-4 are risk factors for cancers, whereas the -1722T/C polymorphism is not associated with an increased risk of cancer.

Nuclear Factor I genomic binding associates with chromatin boundaries

Background

The Nuclear Factor I (NFI) family of DNA binding proteins (also called CCAAT box transcription factors or CTF) is involved in both DNA replication and gene expression regulation. Using chromatin immuno-precipitation and high throughput sequencing (ChIP-Seq), we performed a genome-wide mapping of NFI DNA binding sites in primary mouse embryonic fibroblasts.

Results

We found that in vivo and in vitro NFI DNA binding specificities are indistinguishable, as in vivo ChIP-Seq NFI binding sites matched predictions based on previously established position weight matrix models of its in vitro binding specificity. Combining ChIP-Seq with mRNA profiling data, we found that NFI preferentially associates with highly expressed genes that it up-regulates, while binding sites were under-represented at expressed but unregulated genes. Genomic binding also correlated with markers of transcribed genes such as histone modifications H3K4me3 and H3K36me3, even outside of annotated transcribed loci, implying NFI in the control of the deposition of these modifications. Positional correlation between + and - strand ChIP-Seq tags revealed that, in contrast to other transcription factors, NFI associates with a nucleosomal length of cleavage-resistant DNA, suggesting an interaction with positioned nucleosomes. In addition, NFI binding prominently occurred at boundaries displaying discontinuities in histone modifications specific of expressed and silent chromatin, such as loci submitted to parental allele-specific imprinted expression.

Conclusions

Our data thus suggest that NFI nucleosomal interaction may contribute to the partitioning of distinct chromatin domains and to epigenetic gene expression regulation.

NFI ChIP-Seq and input control DNA data were deposited at Gene Expression Omnibus (GEO) repository under accession number GSE15844. Gene expression microarray data for mouse embryonic fibroblasts are on GEO accession number GSE15871.

Personalized approach of medication by indirect anticoagulants tailored to the patient-Russian context: what are the prospects?

Indirect anticoagulants such as warfarin are the ‘gold standard’ for prevention and treatment of thromboembolic complications in patients at risk (in atrial fibrillation of valvular and nonvalvular etiology, the presence of artificial heart valves, orthopedic and trauma interventions, and other pathological conditions). A wide range of doses required to achieve a therapeutic effect indicates the need for a personalized approach to the appointment of warfarin. In addition to the dependence on the patient's clinical characteristics (sex, age, smoking status, diagnosis), there is a clear association between the warfarin dose and the carriage of certain allelic variants of key genes that makes it possible to apply molecular genetic testing for individual dose adjustment. This provides a more rapid target anticoagulant effect and also reduces the risk of bleeding associated with a possible overdose of warfarin. Implementation of this approach will allow more wide and safe application of indirect anticoagulants in Russia for needy patients.

A regulatory potential of the Xist gene promoter in vole M. rossiaemeridionalis

X chromosome inactivation takes place in the early development of female mammals and depends on the Xist gene expression. The mechanisms of Xist expression regulation have not been well understood so far. In this work, we compared Xist promoter region of vole Microtus rossiaemeridionalis and other mammalian species. We observed three conserved regions which were characterized by computational analysis, DNaseI in vitro footprinting, and reporter construct assay. Regulatory factors potentially involved in Xist activation and repression in voles were determined. The role of CpG methylation in vole Xist expression regulation was established. A CTCF binding site was found in the 5' flanking region of the Xist promoter on the active X chromosome in both males and females. We suggest that CTCF acts as an insulator which defines an inactive Xist domain on the active X chromosome in voles.

Polymorphisms of beta-lactoglobulin promoter region in three Sicilian goat breeds

Several beta-lactoglobulin (BLG) polymorphisms have been described within the proximal promoter region and coding region of the caprine gene, although no genetic variants affecting the protein amino acid composition and/or expression level have been characterized so far. Binding sites for several transcription factors (TFs) are present in the BLG promoter region. The aims of this work were to sequence the full-length promoter region of three Sicilian goat breeds in order to identify polymorphisms, analyze the identified haplotypes, search for differences between breeds for the presence of polymorphisms in this gene region, search for putative TFs binding sites, and check if polymorphisms lay within the identified TFs binding sites. The promoter region of BLG gene in Sicilian goat breeds showed high level of polymorphism due to the presence of 36 single nucleotide polymorphisms (SNPs). Association between polymorphic sites was computed within the whole sample analyzed and 18 haplotypes were inferred. Binding sites for three milk protein binding factors (MPBFs) and four nuclear factor-I (NF-I) were found within BLG promoter region based on the ovine sequence. The identification of some SNPs within TFs binding sites allowed hypothesizing the loss of TFs. Further studies are in progress to evaluate the effect of these mutations on binding affinity of TFs, the functional interaction of the TFs with the goat BLG promoter, and the relationship of the polymorphisms with BLG gene expression and milk production and composition.

Osteopontin: an early innate immune marker of Escherichia coli mastitis harbors genetic polymorphisms with possible links with resistance to mastitis

Background

Mastitis is the most important disease in dairy cows and it causes significant lost of profit to producers. Identification of the genes, and their variants, involved in innate immune responses is essential for the understanding of this inflammatory disease and to identify potential genetic markers for resistance to mastitis. The progeny of dairy cows would benefit from receiving favourable alleles that support greater resistance to infection, thus reducing antibiotic use. This study aims to identify a key gene in the innate immune response to mastitis, led us to evaluate its genetic association with somatic cell score (SCS), which is an indicator of clinical mastitis, and to evaluate its impact on other traits related to milk production.

Results

The osteopontin transcript (SPP1) was identified in the somatic cells from cows experimentally infected with Escherichia coli. By selecting bulls with extreme estimated breeding values (EBVs) for SCS, which is an indicator of mammary gland health, four DNA polymorphisms in the SPP1 genomic sequence were found. Statistical analysis revealed that the SNP SPP1c.-1301G>A has an impact on EBV for SCS (P < 0.001) Using an allele substitution model, SPP1c.-1251C>T, SPP1c.-430G>A, and SPP1c.*40A>C have an impact on SCS whereas SPP1c.-1301G>A has an effect on the EBVs for milk yield (second and third lactations), fat and protein percentages (all three lactations). Analysis revealed statistically significant differences between haplotype groups at a comparison-wise level with sire EBVS for SCS for the first (P = 0.012), second (P < 0.001), and third (P < 0.001) lactations.

Conclusion

This study reports the link between DNA polymorphisms of SPP1, the number of milk immune cells and, potentially, the susceptibility to mastitis. These SNPs were identified by in silico search to be located in transcription factor recognition sites which factors are presumably involved in the Th1 immune response and in the Th2 regulation pathway. Indeed, one SNP abolished the SP1 recognition site, whereas another SNP affected the transcription binding factor IKAROS. All together, these findings support the genetic potential of these variants in terms of selection for the improvement of mastitis resistance in dairy cows.

Nuclear factor I-C links platelet-derived growth factor and transforming growth factor beta1 signaling to skin wound healing progression

Transforming growth factor beta (TGF-beta) and platelet-derived growth factor A (PDGFAlpha) play a central role in tissue morphogenesis and repair, but their interplay remain poorly understood. The nuclear factor I C (NFI-C) transcription factor has been implicated in TGF-beta signaling, extracellular matrix deposition, and skin appendage pathologies, but a potential role in skin morphogenesis or healing had not been assessed. To evaluate this possibility, we performed a global gene expression analysis in NFI-C(-/-) and wild-type embryonic primary murine fibroblasts. This indicated that NFI-C acts mostly to repress gene expression in response to TGF-beta1. Misregulated genes were prominently overrepresented by regulators of connective tissue inflammation and repair. In vivo skin healing revealed a faster inflammatory stage and wound closure in NFI-C(-/-) mice. Expression of PDGFA and PDGF-receptor alpha were increased in wounds of NFI-C(-/-) mice, explaining the early recruitment of macrophages and fibroblasts. Differentiation of fibroblasts to contractile myofibroblasts was also elevated, providing a rationale for faster wound closure. Taken together with the role of TGF-beta in myofibroblast differentiation, our results imply a central role of NFI-C in the interplay of the two signaling pathways and in regulation of the progression of tissue regeneration.

NFI-A directs the fate of hematopoietic progenitors to the erythroid or granulocytic lineage and controls beta-globin and G-CSF receptor expression

It is generally conceded that selective combinations of transcription factors determine hematopoietic lineage commitment and differentiation. Here we show that in normal human hematopoiesis the transcription factor nuclear factor I-A (NFI-A) exhibits a marked lineage-specific expression pattern: it is upmodulated in the erythroid (E) lineage while fully suppressed in the granulopoietic (G) series. In unilineage E culture of hematopoietic progenitor cells (HPCs), NFI-A overexpression or knockdown accelerates or blocks erythropoiesis, respectively: notably, NFI-A overexpression restores E differentiation in the presence of low or minimal erythropoietin stimulus. Conversely, NFI-A ectopic expression in unilineage G culture induces a sharp inhibition of granulopoiesis. Finally, in bilineage E + G culture, NFI-A overexpression or suppression drives HPCs into the E or G differentiation pathways, respectively. These NFI-A actions are mediated, at least in part, by a dual and opposite transcriptional action: direct binding and activation or repression of the promoters of the beta-globin and G-CSF receptor gene, respectively. Altogether, these results indicate that, in early hematopoiesis, the NFI-A expression level acts as a novel factor channeling HPCs into either the E or G lineage.

Thermogenomics: thermodynamic-based approaches to genomic analyses of DNA structure

The postgenomic era is all about learning about function by comparing genomic sequences within and between organisms. This review describes an approach that applies detailed thermodynamic information, as opposed to sequence motif searches, to analyze genomes (thermogenomics) for the occurrence of sequences with the potential to form left-handed Z-DNA and those that bind the eukaryotic nuclear factor I (NFI) transcriptional regulators. Such thermogenomic strategies allow us to address the questions of whether Z-DNA forming sequences can potentially function in regulating transcription of eukaryotic genes and how such function may emerge relative to other GC-rich elements, such as NFI recognition sites, to become a transcriptional coactivator.

Variations in the ghrelin receptor gene associate with obesity and glucose metabolism in individuals with impaired glucose tolerance

BACKGROUND

Ghrelin may influence the development of obesity through its role in the control of energy balance, food intake, and regulation of body weight. The effects of ghrelin are mediated via the growth hormone secretagogue receptor (GHSR).

METHODOLOGY/PRINCIPAL FINDINGS

We genotyped 7 single nucleotide polymorphisms (SNPs) in the GHSR gene and assessed the association between those SNPs and obesity and type 2 diabetes-related phenotypes from 507 middle-aged overweight persons with impaired glucose tolerance participating in the Finnish Diabetes Prevention Study (DPS). Additionally, we performed in silico screening of the 5'-regulatory region of GHSR and evaluated SNPs disrupting putative transcription factor (TF) binding sites in vitro with gelshift assays to determine differences in protein binding between different alleles of SNPs. Rs9819506 in the promoter region of GHSR was associated with body weight (p = 0.036); persons with rs9819506-AA genotype having the lowest body weight. Individuals with rs490683-CC genotype displayed highest weight loss in the whole study population (p = 0.032). The false discovery rate for these results was <10%. Rs490683 and rs509035 were associated with several measures of glucose and insulin metabolism during the follow-up. Rs490683 may be a functional SNP, since gelshift experiments showed differential protein binding between the alleles, with higher binding to the G-allele. Rs490683-C may disrupt a putative binding site for the TF nuclear factor 1 (NF-1), thus rs4906863-GG genotype where the NF-1 site is intact may lead to a higher GHSR gene expression.

CONCLUSION/SIGNIFICANCE

Polymorphisms in the GHSR promoter may modify changes in body weight during long-term lifestyle intervention and affect ghrelin receptor signalling through modulation of GHSR gene expression.

DNA-binding transcription factor NF-1A negatively regulates JC virus multiplication

JC virus (JCV) DNA replication occurs in the nuclei of infected cells. The level of JCV genome expression depends on nucleotide sequences in the viral regulatory region and their interaction with host-cell nuclear transcription factors. Our previous studies showed a higher level of NF-1X in JCV-permissive cells compared with the other members of the NF-1 family, NF-1A, B and C, which suggests that NF-1X plays a positive role in JCV multiplication. It remained unclear whether a reduction in the level of NF-1A, which is expressed abundantly in JCV-non-permissive cell types, leads to an increase in JCV multiplication. In this study, we show that downregulation of NF-1A expression in JCV-non-susceptible progenitor and HeLa cells results in a reversion to susceptibility for JCV multiplication. These data demonstrate that a higher level of NF-1A protein in JCV-non-permissive cell types, compared with the level of NF-1X, may be acting as a negative regulator at the JCV promoter to control JCV multiplication.

Two SNPs in the promoter region of the CTLA-4 gene affect binding of transcription factors and are associated with human myasthenia gravis

OBJECTIVES

The molecular mechanisms underlying the regulation of the CD152 (CTLA-4) gene are largely unknown. Two single nucleotide polymorphisms (SNPs) located in the promoter region are suspected to contribute to the pathogenesis of myasthenia gravis (MG) through regulation of gene expression. SETTING, SUBJECTS AND DESIGN: One hundred and sixty-five unrelated Swedish-Caucasian patients with MG (103 females and 62 males, age 17 to 92 years) and 148 ethnically matched healthy individuals were studied. Gene typing of two SNPs (T/C(-1772) and A/G(-1661)) and quantification of soluble CD152 were performed in the patients. Besides the association studies, the function of these two SNPs is characterized.

RESULTS

We present new genetic associations of two SNPs in the CD152 gene with human MG. These SNPs located in the promoter region are involved in transcriptional binding activity for Nuclear Factor I (NF-1) and c/EBPbeta, as demonstrated using chromatin immunoprecipitation and electromobility shift assay. MG patients with the T/C(-1772) polymorphism have elevated levels of sCD152 in sera.

CONCLUSIONS

The two SNPs in the promoter region are associated with MG and might cause abnormal alternative splicing and affect the expression of CD152, thereby contributing to the pathogenesis of MG.

Peroxisome proliferator-activated receptor subtypes differentially cooperate with other transcription factors in selective transactivation of the perilipin/PEX11 alpha gene pair

Perilipin is an adipocyte-specific protein associated with lipid droplets that is crucial for the regulation of storage and mobilization of lipids. We earlier reported that the mouse perilipin gene is regulated by peroxisome proliferator-activated receptor (PPAR) gamma through a peroxisome proliferator-response element (PPRE) positioned upstream of the perilipin promoter. Moreover, we showed that this PPRE also controls expression of the PEX11alpha gene, which is located further upstream. We show here that three elements, A, B, and C, in close proximity downstream of the PPRE, are essential for transactivation of the perilipin gene by PPARgamma. Electrophoretic gel-mobility shift assays demonstrated that nuclear factor (NF)-1 subtypes bind specifically to element B. Furthermore, chromatin immunoprecipitation using 3T3-L1 cells revealed that NF-1A and NF-1B bind to element B in a differentiation-dependent fashion, whereas binding is constitutive with NF-1C and NF-1X. Element C is likely to be a binding motif for nuclear receptors. With PPARalpha, elements A-C do not appear to be required for transactivation of the PEX11alpha gene, so that cooperation with other transcription factors may be differentially involved in selective transactivation of the PEX11alpha and perilipin genes by different PPAR subtypes.

Gene expression analysis of nuclear factor I-A deficient mice indicates delayed brain maturation

Gene expression analysis of brains from mice deficient in nuclear factor I-A (Nfia^-/- mice) and from Nfia^+/+ mice suggests that Nfia^-/- mice are delayed in early postnatal development, especially oligodendrocyte maturation.

Background

Nuclear factor I-A (NFI-A), a phylogenetically conserved transcription/replication protein, plays a crucial role in mouse brain development. Previous studies have shown that disruption of the Nfia gene in mice leads to perinatal lethality, corpus callosum agenesis, and hydrocephalus.

Results

To identify potential NFI-A target genes involved in the observed tissue malformations, we analyzed gene expression in brains from Nfia^-/- and Nfia^+/+ littermate mice at the mRNA level using oligonucleotide microarrays. In young postnatal animals (postnatal day 16), 356 genes were identified as being differentially regulated, whereas at the late embryonic stage (embryonic day 18) only five dysregulated genes were found. An in silico analysis identified phylogenetically conserved NFI binding sites in at least 70 of the differentially regulated genes. Moreover, assignment of gene function showed that marker genes for immature neural cells and neural precursors were expressed at elevated levels in young postnatal Nfia^-/- mice. In contrast, marker genes for differentiated neural cells were downregulated at this stage. In particular, genes relevant for oligodendrocyte differentiation were affected.

Conclusion

Our findings suggest that brain development, especially oligodendrocyte maturation, is delayed in Nfia^-/- mice during the early postnatal period, which at least partly accounts for their phenotype. The identification of potential NFI-A target genes in our study should help to elucidate NFI-A dependent transcriptional pathways and contribute to enhanced understanding of this period of brain formation, especially with regard to the function of NFI-A.

Vascular endothelial growth factor expression in arthritic joint is regulated by SAF-1 transcription factor

Vascular endothelial growth factor (VEGF) plays an important role in the pathogenesis of arthritis by promoting angiogenesis in the synovial joint and infiltration of inflammatory cells in the synovial joint. Although ample information has been obtained on the mechanism of VEGF regulation during cancer and hypoxic condition, less is known about the control of VEGF expression during arthritis. From the studies on the experimentally induced arthritis in a transgenic mouse model that overexpresses a transcription factor, serum amyloid A activating factor-1 (SAF-1), leading to markedly higher levels of angiogenesis, synovial inflammation, and inflammatory cell infiltration, we have identified a novel mechanism of VEGF regulation. We present molecular evidence that VEGF expression is increased in SAF-1-transgenic mice and that SAF-1 induces VEGF transcription by directly binding to its promoter. Deletion of SAF-1 binding elements from the VEGF promoter as well as knockdown of endogenous SAF-1 markedly inhibited IL-1beta- and TGF-beta-mediated induction of VEGF expression in chondrocyte cells. By chromatin immunoprecipitation assay, in vivo, markedly higher levels of SAF-1 interaction with the VEGF promoter was detected in the cartilage tissues of arthritic mice as well as human osteoarthritic patients. Together, these results provide a new insight into the molecular mechanism of VEGF expression.

Smads orchestrate specific histone modifications and chromatin remodeling to activate transcription

Smads are intracellular transducers for TGF-beta superfamily ligands, but little is known about the mechanism by which complexes of receptor-phosphorylated Smad2 and Smad4 regulate transcription. Using an in vitro transcription system, we have discovered that, unlike most transcription factors that are sufficient to recruit the basal transcription machinery and therefore activate transcription on both naked DNA and chromatin templates, the Smads only activate transcription from chromatin templates. We demonstrate that Smad2-mediated transcription requires the histone acetyltransferase, p300. Smad2-recruited p300 exhibits an altered substrate specificity, specifically acetylating nucleosomal histone H3 at lysines 9 and 18, and these modifications are also detected on an endogenous Smad2-dependent promoter in a ligand-induced manner. Furthermore, we show that endogenous Smad2 interacts with the SWI/SNF ATPase, Brg1, in a TGF-beta-dependent manner, and demonstrate that Brg1 is recruited to Smad2-dependent promoters and is specifically required for TGF-beta-induced expression of endogenous Smad2 target genes. Our data indicate that the Smads define a new class of transcription factors that absolutely require chromatin to assemble the basal transcription machinery and activate transcription.

A novel functional VKORC1 promoter polymorphism is associated with inter-individual and inter-ethnic differences in warfarin sensitivity

Warfarin, a commonly prescribed anticoagulant, exhibited large inter-individual and inter-ethnic differences in the dose required for its anticoagulation effect. Asian populations, including Chinese, require a much lower maintenance dose than Caucasians, for which the mechanisms still remain unknown. We determined DNA sequence variants in CYP2C9 and VKORC1 in 16 Chinese patients having warfarin sensitivity (< or = 1.5 mg/day, n = 11) or resistance (> or = 6.0 mg/day, n = 5), 104 randomly selected Chinese patients receiving warfarin, 95 normal Chinese controls and 92 normal Caucasians. We identified three CYP2C9 variants, CYP2C9*3, T299A and P382L, in four warfarin-sensitive patients. A novel VKORC1 promoter polymorphism (-1639 G > A) presented in the homozygous form (genotype AA) was found in all warfarin-sensitive patients. The resistant patients were either AG or GG. Among the 104 randomly selected Chinese patients receiving warfarin, AA genotype also had lower dose than the AG/GG genotype (P < 0.0001). Frequencies of AA, AG and GG genotypes were comparable in Chinese patients receiving warfarin (79.7, 17.6 and 2.7%) and normal Chinese controls (82, 18 and 0%), but differed significantly from Caucasians (14, 47 and 39%) (P < 0.0001). The promoter polymorphism abolished the E-box consensus sequences and dual luciferase assay revealed that VOKRC1 promoter with the G allele had a 44% increase of activity when compared with the A allele. The differences in allele frequencies of A/G allele and its levels of VKORC1 promoter activity may underscore the inter-individual differences in warfarin dosage as well as inter-ethnic differences between Chinese and Caucasians.

Transcription factor YB-1 mediates DNA polymerase alpha gene expression

Y-box protein-1 involvement in cyclin A and B1 gene regulation has recently been demonstrated. A more generalized role of this protein for cell replication is hypothesized as numerous regulatory sequences of cell cycle-related genes contain putative binding sites. In the present study the DNA polymerase alpha (DPA) gene is identified as another YB-1-responsive gene with a Y-box and 3' inverted repeat sequence, designated DPA RE-1, in the serum-responsive promoter region. Overexpressed YB-1 concentration-dependently trans-activated DPA gene expression in reporter assays and Southwestern blotting as well as DNA binding analyses revealed binding of distinct endogenous proteins to the RE-1 with molecular sizes of 26, 32 and 52 kDa. Among these, YB-1 binding was confirmed using recombinant as well as endogenous proteins, with preferential single-stranded DNA binding. Early serum growth response in mesangial cells was accompanied by a nuclear YB-1 shift and nucleocomplex formation at the RE-1. Fine mapping of the DPA RE-1 sequence unraveled a dependence on co-factors for trans-regulation with gene activation in the context of a heterologous SV40 promoter but suppression in the context of the abbreviated homologous promoter sequence. A YB-1 knock down resulted in decreased DPA transcription rates and abrogated the serum-dependent induction of DPA transcription. These results link YB-1 with serum responsiveness of DPA gene expression and provide insight into the required sequence and protein binding context.

Phylogenetic footprinting reveals evolutionarily conserved regions of the gonadotropin-releasing hormone gene that enhance cell-specific expression

Reproductive function is controlled by the hypothalamic neuropeptide, GnRH, which serves as the central regulator of the hypothalamic-pituitary-gonadal axis. GnRH expression is limited to a small population of neurons in the hypothalamus. Targeting this minute population of neurons (as few as 800 in the mouse) requires regulatory elements upstream of the GnRH gene that remain to be fully characterized. Previously, we have identified an evolutionarily conserved promoter region (-173 to +1) and an enhancer (-1863 to -1571) in the rat gene that targets a subset of the GnRH neurons in vivo. In the present study, we used phylogenetic sequence comparison between human and rodents and analysis of the transcription factor clusters within conserved regions in an attempt to identify additional upstream regulatory elements. This approach led to the characterization of a new upstream enhancer that regulates expression of GnRH in a cell-specific manner. Within this upstream enhancer are nine binding sites for Octamer-binding transcription factor 1 (OCT1), known to be an important transcriptional regulator of GnRH gene expression. In addition, we have identified nuclear factor I (NF1) binding to multiple elements in the GnRH-regulatory regions, each in close proximity to OCT1. We show that OCT1 and NF1 physically and functionally interact. Moreover, the OCT1 and NF1 binding sites in the regulatory regions appear to be essential for appropriate GnRH gene expression. These findings indicate a role for this upstream enhancer and novel OCT1/NF1 complexes in neuron-restricted expression of the GnRH gene.